
@unknown{tang2022textbox,
author = {Tang, Tianyi and Junyi, Li and Chen, Zhipeng and Hu, Yiwen and Yu, Zhuohao and Dai, Wenxun and Dong, Zican and Cheng, Xiaoxue and Wang, Yuhao and Zhao, Wayne and Nie, Jian-yun and Wen, Ji-Rong},
year = {2022},
month = {12},
pages = {},
title = {TextBox 2.0: A Text Generation Library with Pre-trained Language Models}
}

% prompt ide
@misc{strobelt2022promptide,
    doi = {10.48550/ARXIV.2208.07852},
    url = {https://arxiv.org/abs/2208.07852},
    author = {Strobelt, Hendrik and Webson, Albert and Sanh, Victor and Hoover, Benjamin and Beyer, Johanna and Pfister, Hanspeter and Rush, Alexander M.},
    keywords = {Computation and Language (cs.CL), Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models},
    publisher = {arXiv},
    year = {2022}
  }

% prompt source
@misc{bach2022promptsource,
      title={PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts},
      author={Stephen H. Bach and Victor Sanh and Zheng-Xin Yong and Albert Webson and Colin Raffel and Nihal V. Nayak and Abheesht Sharma and Taewoon Kim and M Saiful Bari and Thibault Fevry and Zaid Alyafeai and Manan Dey and Andrea Santilli and Zhiqing Sun and Srulik Ben-David and Canwen Xu and Gunjan Chhablani and Han Wang and Jason Alan Fries and Maged S. Al-shaibani and Shanya Sharma and Urmish Thakker and Khalid Almubarak and Xiangru Tang and Xiangru Tang and Mike Tian-Jian Jiang and Alexander M. Rush},
      year={2022},
      eprint={2202.01279},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

% prompt chainer
@misc{wu2022promptchainer,
    title={PromptChainer: Chaining Large Language Model Prompts through Visual Programming},
    author={Tongshuang Wu and Ellen Jiang and Aaron Donsbach and Jeff Gray and Alejandra Molina and Michael Terry and Carrie J Cai},
    year={2022},
    eprint={2203.06566},
    archivePrefix={arXiv},
    primaryClass={cs.HC}
}

% openprompt
@article{ding2021openprompt,
  title={OpenPrompt: An Open-source Framework for Prompt-learning},
  author={Ding, Ning and Hu, Shengding and Zhao, Weilin and Chen, Yulin and Liu, Zhiyuan and Zheng, Hai-Tao and Sun, Maosong},
  journal={arXiv preprint arXiv:2111.01998},
  year={2021}
}

% PromptMaker
@inproceedings{jiang2022promptmaker,
    author = {Jiang, Ellen and Olson, Kristen and Toh, Edwin and Molina, Alejandra and Donsbach, Aaron and Terry, Michael and Cai, Carrie J},
    title = {PromptMaker: Prompt-Based Prototyping with Large&nbsp;Language&nbsp;Models},
    year = {2022},
    isbn = {9781450391566},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3491101.3503564},
    doi = {10.1145/3491101.3503564},
    abstract = {Prototyping is notoriously difficult to do with machine learning (ML), but recent advances in large language models may lower the barriers to people prototyping with ML, through the use of natural language prompts. This case study reports on the real-world experiences of industry professionals (e.g. designers, program managers, front-end developers) prototyping new ML-powered feature ideas via prompt-based prototyping. Through interviews with eleven practitioners during a three-week sprint and a workshop, we find that prompt-based prototyping reduced barriers of access by substantially broadening who can prototype with ML, sped up the prototyping process, and grounded communication between collaborators. Yet, it also introduced new challenges, such as the need to reverse-engineer prompt designs, source example data, and debug and evaluate prompt effectiveness. Taken together, this case study provides important implications that lay the groundwork toward a new future of prototyping with ML.},
    booktitle = {Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems},
    articleno = {35},
    numpages = {8},
    location = {New Orleans, LA, USA},
    series = {CHI EA '22}
}